{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T02:32:46Z","timestamp":1774665166841,"version":"3.50.1"},"reference-count":86,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deputyship for Research and Innovation, \u201cMinistry of Education\u201d in Saudi Arabia","award":["IFKSUOR3-574-1"],"award-info":[{"award-number":["IFKSUOR3-574-1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of machine learning models. Achieving optimal hyperparameter settings often requires a deep understanding of the underlying models and the appropriate optimization techniques. While there are many automatic optimization techniques available, each with its own advantages and disadvantages, this article focuses on hyperparameter optimization for well-known machine learning models. It explores cutting-edge optimization methods such as metaheuristic algorithms, deep learning-based optimization, Bayesian optimization, and quantum optimization, and our paper focused mainly on metaheuristic and Bayesian optimization techniques and provides guidance on applying them to different machine learning algorithms. The article also presents real-world applications of hyperparameter optimization by conducting tests on spatial data collections for landslide susceptibility mapping. Based on the experiment\u2019s results, both Bayesian optimization and metaheuristic algorithms showed promising performance compared to baseline algorithms. For instance, the metaheuristic algorithm boosted the random forest model\u2019s overall accuracy by 5% and 3%, respectively, from baseline optimization methods GS and RS, and by 4% and 2% from baseline optimization methods GA and PSO. Additionally, for models like KNN and SVM, Bayesian methods with Gaussian processes had good results. When compared to the baseline algorithms RS and GS, the accuracy of the KNN model was enhanced by BO-TPE by 1% and 11%, respectively, and by BO-GP by 2% and 12%, respectively. For SVM, BO-TPE outperformed GS and RS by 6% in terms of performance, while BO-GP improved results by 5%. The paper thoroughly discusses the reasons behind the efficiency of these algorithms. By successfully identifying appropriate hyperparameter configurations, this research paper aims to assist researchers, spatial data analysts, and industrial users in developing machine learning models more effectively. The findings and insights provided in this paper can contribute to enhancing the performance and applicability of machine learning algorithms in various domains.<\/jats:p>","DOI":"10.3390\/s23156843","type":"journal-article","created":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T09:32:35Z","timestamp":1690882355000},"page":"6843","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques"],"prefix":"10.3390","volume":"23","author":[{"given":"Farkhanda","family":"Abbas","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Muhammad","family":"Ismail","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Karakoram International University, Gilgit 15100, Pakistan"}]},{"given":"Garee","family":"Khan","sequence":"additional","affiliation":[{"name":"School of Geography, Karakoram International University, Gilgit 15100, Pakistan"}]},{"given":"Javed","family":"Iqbal","sequence":"additional","affiliation":[{"name":"School of Environmental Studies, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3761-6656","authenticated-orcid":false,"given":"Abdulwahed Fahad","family":"Alrefaei","sequence":"additional","affiliation":[{"name":"Department of Zoology, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia"}]},{"given":"Mohammed Fahad","family":"Albeshr","sequence":"additional","affiliation":[{"name":"Department of Zoology, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,1]]},"reference":[{"key":"ref_1","first-page":"684","article-title":"Add a new comment","volume":"346","author":"Polanco","year":"2014","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1613\/jair.1.11854","article-title":"Benchmark and survey of automated machine learning frameworks","volume":"70","author":"Huber","year":"2021","journal-title":"J. Artif. Intell. Res."},{"key":"ref_3","unstructured":"Elshawi, R., Maher, M., and Sakr, S. (2019). 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